{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6f9cba4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "062f56e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.6.0'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ee5eb24a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.test.is_gpu_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bf082ce9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.6.0\n",
      "    Education     Income\n",
      "0   10.000000  26.658839\n",
      "1   10.401338  27.306435\n",
      "2   10.842809  22.132410\n",
      "3   11.244147  21.169841\n",
      "4   11.645449  15.192634\n",
      "5   12.086957  26.398951\n",
      "6   12.048829  17.435307\n",
      "7   12.889632  25.507885\n",
      "8   13.290970  36.884595\n",
      "9   13.732441  39.666109\n",
      "10  14.133779  34.396281\n",
      "11  14.635117  41.497994\n",
      "12  14.978589  44.981575\n",
      "13  15.377926  47.039595\n",
      "14  15.779264  48.252578\n",
      "15  16.220736  57.034251\n",
      "16  16.622074  51.490919\n",
      "17  17.023411  51.336621\n",
      "18  17.464883  57.681998\n",
      "19  17.866221  68.553714\n",
      "20  18.267559  64.310925\n",
      "21  18.709030  68.959009\n",
      "22  19.110368  74.614639\n",
      "23  19.511706  71.867195\n",
      "24  19.913043  76.098135\n",
      "25  20.354515  75.775216\n",
      "26  20.755853  72.486055\n",
      "27  21.167191  77.355021\n",
      "28  21.598662  72.118790\n",
      "29  22.000000  80.260571\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.__version__)\n",
    "\n",
    "#  使用tf.keras 实现简单线性回归\n",
    "import pandas as pd\n",
    "data = pd.read_csv(\"../dataset/Income.csv\")\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ee6ee1f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b94d3116",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x20e06206cc0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAATa0lEQVR4nO3de4wdZ3nH8e/TXMQSKJuLcZ1NUrsFgihuYtimoNAWEkLSFhErQogKVaaNZAkhyk0hCZVASK1sCILyV1uraeOqESQNJolAhbq5UPEHRus4ITeoQ0ggGydeIG4ptSihT//YMXE2u945uzNn5j3n+5GsPWfO2ZxnYs9vZ59533ciM5EkleeXui5AkrQyBrgkFcoAl6RCGeCSVCgDXJIKdfwwP+y0007L9evXD/MjJal4e/fu/UFmrlm4fagBvn79emZmZob5kZJUvIh4dLHttlAkqVAGuCQVygCXpEIZ4JJUKANckgo11FEokjRObt43yzVf+TaPHzrM6ZMTXHHx2WzeNNXYf7/WGXhEvD8i7o+I+yLisxHxvIjYEBF7IuKhiLghIk5srCpJKtzN+2a5ete9zB46TAKzhw5z9a57uXnfbGOfsWyAR8QU8GfAdGa+EjgOeDvwceDTmfkS4Cng8saqkqTCXfOVb3P4Zz9/1rbDP/s513zl2419Rt0e+PHAREQcDzwfOABcANxUvb4T2NxYVZJUuMcPHR5o+0osG+CZOQt8Evge88H9n8Be4FBmPl297TFg0cZORGyNiJmImJmbm2umaknqudMnJwbavhJ1WignA5cCG4DTgZOAS+p+QGbuyMzpzJxes+Y5U/klaSRdcfHZTJxw3LO2TZxwHFdcfHZjn1FnFMobge9m5hxAROwCzgcmI+L46iz8DKC5zrwkFe7IaJM2R6HUCfDvAa+JiOcDh4ELgRngDuCtwOeALcAtjVUlSSNg86apRgN7oTo98D3MX6y8C7i3+p4dwJXAByLiIeBU4NrWqpQkPUetiTyZ+VHgows2Pwyc13hFktSBtifdtMGZmJLG3pFJN0fGbR+ZdAP0OsRdC0XS2BvGpJs2GOCSxt4wJt20wRaKpF7osgd9+uQEs4uEdZOTbtrgGbikzq1k4aeb981y/vbb2XDVlzh/++2rWiRqGJNu2mCAS+rcoD3oplf627xpim2XbWRqcoIApiYn2HbZxl5fwARbKJJ6YNAe9LECf6Wh2/akmzYY4JJWpMme9aA96FIvOjbNFoqkgTXdwhi0Bz2Mlf5KYIBLGljT46YH7UGXetGxabZQJA2sjRbGID3oYaz0VwIDXNLA+jBuusSLjk2zhSJpYLYw+sEzcEkDs4XRDwa4pBUppYVR4jKxdRngkkZWW8vE9uWHggEuqVVdhl0bMzb7tHa4FzEltabpCT+DamO4Y5/WDjfAJbWm67BrY8Zmn6bxG+CSWtN12LUx3LFP0/gNcEmt6Trs2lgmtk9j4Je9iBkRZwM3HLXp14CPAP9YbV8PPAK8LTOfar5ESaW64uKzn3XBD4Yfdk0Pd+zTGPjIzPpvjjgOmAV+G3g38KPM3B4RVwEnZ+aVx/r+6enpnJmZWU29kgrTlyF3JYuIvZk5vXD7oMMILwS+k5mPRsSlwOur7TuBO4FjBrik8VPKhJ8SDdoDfzvw2erx2sw8UD1+Ali72DdExNaImImImbm5uRWWKUlaqPYZeEScCLwFuHrha5mZEbFoLyYzdwA7YL6FssI6JQ2JLY9yDNJC+X3grsx8snr+ZESsy8wDEbEOONh8eZKOpemw7dMsQy1vkBbKH/FM+wTgVmBL9XgLcEtTRUlaXhuzHLueeKPB1ArwiDgJuAjYddTm7cBFEbEfeGP1XNKQtBG2XU+80WBqtVAy8yfAqQu2/ZD5USmSOtBG2PbhTjuqz5mYUqHamOXYp1mGWp4BLhWqjbBtY+q52uN64FKh2prS7cSbchjgUsEM2/FmC0WSCmWAS1KhDHBJKpQBLkmFMsAlqVCOQpHGgCsMjiYDXBpxrjA4umyhSCPOFQZHlwEujThXGBxdBrg04tpY9Er9YIBLI84VBkeXFzGlEdfWolfqngEujQEXvRpNtlAkqVAGuCQVygCXpEIZ4JJUKANckgpVK8AjYjIiboqIb0XEgxHx2og4JSJ2R8T+6uvJbRcrSXpG3TPwzwBfzsyXA+cADwJXAbdl5kuB26rnkqQhWTbAI+JFwO8C1wJk5v9m5iHgUmBn9badwOZ2SpQkLabOGfgGYA74h4jYFxF/FxEnAWsz80D1nieAtYt9c0RsjYiZiJiZm5trpmpJUq0APx54FfDXmbkJ+AkL2iWZmUAu9s2ZuSMzpzNzes2aNautV5JUqRPgjwGPZeae6vlNzAf6kxGxDqD6erCdEiVJi1k2wDPzCeD7EXFk6bILgQeAW4Et1bYtwC2tVChJWlTdxazeA1wfEScCDwN/wnz43xgRlwOPAm9rp0RpvHj/StVVK8Az825gepGXLmy0GmnMef9KDcLlZKVFdHUWfKz7VxrgWsgAlxbo8izY+1dqEK6FIi3Q5V3cvX+lBmGASwt0eRbs/Ss1CANcWqDLs+DNm6bYdtlGpiYnCGBqcoJtl220/61F2QOXFrji4rOf1QOH4Z4Fe/9K1WWASwt4F3eVwgCXFuFZsEpgD1ySCmWAS1KhDHBJKpQBLkmFMsAlqVAGuCQVygCXpEIZ4JJUKANckgrlTExpFere+MHbpKkNBri0QnVv/OBt0tQWWyjSCtW98UOXN4jQaDPApRWqe+MHb5OmttQK8Ih4JCLujYi7I2Km2nZKROyOiP3V15PbLVXql7o3fvA2aWrLIGfgb8jMczNzunp+FXBbZr4UuK16Lo2Nurc/8zZpastqLmJeCry+erwTuBO4cpX1SMWoe+MHbxChtkRmLv+miO8CTwEJ/G1m7oiIQ5k5Wb0ewFNHni/43q3AVoCzzjrr1Y8++mhz1UvSGIiIvUd1P36h7hn46zJzNiJeDOyOiG8d/WJmZkQs+pMgM3cAOwCmp6eX/2khSaqlVoBn5mz19WBEfAE4D3gyItZl5oGIWAccbLFOadWcTKNRs+xFzIg4KSJeeOQx8CbgPuBWYEv1ti3ALW0VKa3Wkck0s4cOkzwzmebmfbNdlyatWJ1RKGuBr0XEPcA3gC9l5peB7cBFEbEfeGP1XOolJ9NoFC3bQsnMh4FzFtn+Q+DCNoqSmuZkGo0i10JR0er2tU+fnGB2kbB2Mo1K5lR6FWuQvraTaTSKDHAVa5C+9uZNU2y7bCNTkxMEMDU5wbbLNjoKRUWzhaJiDdrX3rxpysDWSPEMXMVykSiNOwNcxbKvrXFnC0W9VGd0iYtEadwZ4OqdQW5BZl9b48wWinrHWZNSPQa4esdZk1I9Brh6x9ElUj0GuHrH0SVSPV7EVO84ukSqxwBXLzm6RFqeLRRJKpQBLkmFMsAlqVD2wNUJbzAsrZ4BrqEbZKq8pKXZQtHQOVVeaoYBrqFzqrzUjNoBHhHHRcS+iPhi9XxDROyJiIci4oaIOLG9MjVKnCovNWOQM/D3Ag8e9fzjwKcz8yXAU8DlTRam0eVUeakZtQI8Is4A/hD4u+p5ABcAN1Vv2QlsbqE+dejmfbOcv/12Nlz1Jc7ffvuid3tfCW8wLDWj7iiUvwI+BLywen4qcCgzn66ePwZ49I2QtkeKOFVeWr1lz8Aj4s3Awczcu5IPiIitETETETNzc3Mr+U+oA44UkfqvTgvlfOAtEfEI8DnmWyefASYj4sgZ/BnAor9fZ+aOzJzOzOk1a9Y0ULKGwZEiUv8tG+CZeXVmnpGZ64G3A7dn5juAO4C3Vm/bAtzSWpUaOkeKSP23mnHgVwIfiIiHmO+JX9tMSeoDR4pI/TfQVPrMvBO4s3r8MHBe8yWpD7ypgtR/roWiJTlSROo3A3zMuAqgNDoM8DHiKoDSaHExqzHi2G5ptBjgY8Sx3dJoMcDHiGO7pdFigI8Rx3ZLo8WLmGPEsd3SaDHAx4xju6XRYYBr1RxbLnXDANeqOLZc6o4XMbUqji2XumOAa1UcWy51xwDXqji2XOqOAa5VcWy51B0vYmpVHFsudccA16o5tlzqhi0USSqUAS5JhTLAJalQBrgkFcoAl6RCLRvgEfG8iPhGRNwTEfdHxMeq7RsiYk9EPBQRN0TEie2XK0k6os4Z+E+BCzLzHOBc4JKIeA3wceDTmfkS4Cng8taqlCQ9x7IBnvP+u3p6QvUngQuAm6rtO4HNbRQoSVpcrR54RBwXEXcDB4HdwHeAQ5n5dPWWx4BFZ3JExNaImImImbm5uQZKliRBzQDPzJ9n5rnAGcB5wMvrfkBm7sjM6cycXrNmzcqqlCQ9x0CjUDLzEHAH8FpgMiKOTMU/A5httjRJ0rHUGYWyJiImq8cTwEXAg8wH+Vurt20BbmmpRknSIuosZrUO2BkRxzEf+Ddm5hcj4gHgcxHxF8A+4NoW65QkLbBsgGfmN4FNi2x/mPl+eC94Y11J42YklpP1xrqSxlHvA7zOmfWxbqxrgEsaVb0O8Lpn1qXdWNd2j6Qm9Hoxq2OdWR+tpBvrHvmhNHvoMMkzP5Ru3ucoTEmD6XWA1z2zLunGunV/KEnScnrdQjl9coLZRUJ84Zl1STfWbaPdY0tGGk+9DvArLj77WT1wWPrMupQb69b9oVSXI3Ck8dXrFsrmTVNsu2wjU5MTBDA1OcG2yzYWHUxNt3tsyUjjq9dn4FDOmXVdTbd7ShuBI6k5vQ/wUdTkD6WmWzKSytHrFoqWV9IIHEnN8gy8cCWNwJHULAN8BIzadQJJ9dhCkaRCGeCSVKixbKE4c1HSKBi7AHfmoqRRMXYtFGcuShoVY3cGPujMRdstkvpq7M7AB1k73LW7JfXZ2AX4IDMXbbdI6rNlAzwizoyIOyLigYi4PyLeW20/JSJ2R8T+6uvJ7Ze7eoOscOhCUZL6rE4P/Gngg5l5V0S8ENgbEbuBdwK3Zeb2iLgKuAq4sr1Sm1N35qILRUnqs2XPwDPzQGbeVT3+MfAgMAVcCuys3rYT2NxSjZ1xoShJfTbQKJSIWA9sAvYAazPzQPXSE8DaJb5nK7AV4KyzzlpxoV1woShJfRaZWe+NES8Avgr8ZWbuiohDmTl51OtPZeYx++DT09M5MzOzmnolaexExN7MnF64vdYolIg4Afg8cH1m7qo2PxkR66rX1wEHmypWkrS8OqNQArgWeDAzP3XUS7cCW6rHW4Bbmi9PkrSUOj3w84E/Bu6NiLurbR8GtgM3RsTlwKPA21qpUJK0qGUDPDO/BsQSL1/YbDmjz6n5kpoydmuhdMmVECU1aeym0nfJqfmSmmSAD5FT8yU1yQAfokFWQpSk5RjgQ+TUfElN8iLmEDk1X1KTDPCG1B0eWHclRElajgHeAIcHSuqCPfAGODxQUhcM8AY4PFBSFwzwBjg8UFIXDPAGODxQUhe8iNkAhwdK6oIB3hCHB0oaNlsoklQoA1ySCmWAS1KhDHBJKpQBLkmFiswc3odFzDF/A+SVOA34QYPldGlU9mVU9gPcl74alX1Z7X78amauWbhxqAG+GhExk5nTXdfRhFHZl1HZD3Bf+mpU9qWt/bCFIkmFMsAlqVAlBfiOrgto0Kjsy6jsB7gvfTUq+9LKfhTTA5ckPVtJZ+CSpKMY4JJUqF4GeET8fUQcjIj7jtp2SkTsjoj91deTu6yxriX25ZqI+FZEfDMivhARkx2WWMti+3HUax+MiIyI07qobVBL7UtEvKf6e7k/Ij7RVX2DWOLf17kR8fWIuDsiZiLivC5rrCMizoyIOyLiger//3ur7cUd98fYl8aP+14GOHAdcMmCbVcBt2XmS4HbqucluI7n7stu4JWZ+ZvAfwBXD7uoFbiO5+4HEXEm8Cbge8MuaBWuY8G+RMQbgEuBczLzN4BPdlDXSlzHc/9ePgF8LDPPBT5SPe+7p4EPZuYrgNcA746IV1Dmcb/UvjR+3PcywDPz34EfLdh8KbCzerwT2DzMmlZqsX3JzH/NzKerp18Hzhh6YQNa4u8E4NPAh4BiroYvsS/vArZn5k+r9xwcemErsMS+JPDL1eMXAY8PtagVyMwDmXlX9fjHwIPAFAUe90vtSxvHfS8DfAlrM/NA9fgJYG2XxTToT4F/6bqIlYiIS4HZzLyn61oa8DLgdyJiT0R8NSJ+q+uCVuF9wDUR8X3mf5Mo4Te8X4iI9cAmYA+FH/cL9uVojRz3JQX4L+T82MdizviWEhF/zvyvW9d3XcugIuL5wIeZ/xV9FBwPnML8r7xXADdGRHRb0oq9C3h/Zp4JvB+4tuN6aouIFwCfB96Xmf919GulHfdL7UuTx31JAf5kRKwDqL4W8SvuUiLincCbgXdkmYPxfx3YANwTEY8w/+vgXRHxK51WtXKPAbty3jeA/2N+AaISbQF2VY//Gej9RUyAiDiB+cC7PjOP1F/kcb/EvjR+3JcU4Lcy/w+T6ustHdayKhFxCfN947dk5v90Xc9KZOa9mfnizFyfmeuZD8BXZeYTHZe2UjcDbwCIiJcBJ1LuKniPA79XPb4A2N9hLbVUv+1cCzyYmZ866qXijvul9qWV4z4ze/cH+CxwAPgZ88FwOXAq81eh9wP/BpzSdZ2r2JeHgO8Dd1d//qbrOleyHwtefwQ4res6V/F3ciLwT8B9wF3ABV3XuYp9eR2wF7iH+d7rq7uus8Z+vI759sg3jzou/qDE4/4Y+9L4ce9UekkqVEktFEnSUQxwSSqUAS5JhTLAJalQBrgkFcoAl6RCGeCSVKj/B7ucQXFoqrWTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data.Education,data.Income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "103e0c50",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.Education\n",
    "y = data.Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60e1ed9b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d1360ea3",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e903fabe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.6.0\n",
      "    Education     Income\n",
      "0   10.000000  26.658839\n",
      "1   10.401338  27.306435\n",
      "2   10.842809  22.132410\n",
      "3   11.244147  21.169841\n",
      "4   11.645449  15.192634\n",
      "5   12.086957  26.398951\n",
      "6   12.048829  17.435307\n",
      "7   12.889632  25.507885\n",
      "8   13.290970  36.884595\n",
      "9   13.732441  39.666109\n",
      "10  14.133779  34.396281\n",
      "11  14.635117  41.497994\n",
      "12  14.978589  44.981575\n",
      "13  15.377926  47.039595\n",
      "14  15.779264  48.252578\n",
      "15  16.220736  57.034251\n",
      "16  16.622074  51.490919\n",
      "17  17.023411  51.336621\n",
      "18  17.464883  57.681998\n",
      "19  17.866221  68.553714\n",
      "20  18.267559  64.310925\n",
      "21  18.709030  68.959009\n",
      "22  19.110368  74.614639\n",
      "23  19.511706  71.867195\n",
      "24  19.913043  76.098135\n",
      "25  20.354515  75.775216\n",
      "26  20.755853  72.486055\n",
      "27  21.167191  77.355021\n",
      "28  21.598662  72.118790\n",
      "29  22.000000  80.260571\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 1)                 2         \n",
      "=================================================================\n",
      "Total params: 2\n",
      "Trainable params: 2\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "Epoch 1/5000\n",
      "1/1 [==============================] - 1s 1s/step - loss: 4687.7554\n",
      "Epoch 2/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 4685.4072\n",
      "Epoch 3/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 4683.0601\n",
      "Epoch 4/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 4680.7129\n",
      "Epoch 5/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 4678.3677\n",
      "Epoch 6/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 4676.0220\n",
      "Epoch 7/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 4673.6772\n",
      "Epoch 8/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 4671.3330\n",
      "Epoch 9/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 4668.9902\n",
      "Epoch 10/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 4666.6479\n",
      "Epoch 11/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 4664.3057\n",
      "Epoch 12/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 4661.9634\n",
      "Epoch 13/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 4659.6235\n",
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    {
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     "text": [
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     "text": [
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    {
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     "text": [
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    },
    {
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     "text": [
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     ]
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "text": [
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     ]
    },
    {
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     "text": [
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     "text": [
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     ]
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     "text": [
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     ]
    },
    {
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     ]
    },
    {
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     ]
    },
    {
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     ]
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    {
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     "text": [
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     ]
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    {
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     "text": [
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     ]
    },
    {
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     "text": [
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    {
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     ]
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    {
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     "text": [
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      "1/1 [==============================] - 0s 2ms/step - loss: 154.5732\n",
      "Epoch 4988/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 154.4966\n",
      "Epoch 4989/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 154.4202\n",
      "Epoch 4990/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 154.3439\n",
      "Epoch 4991/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 154.2678\n",
      "Epoch 4992/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 154.1916\n",
      "Epoch 4993/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 154.1157\n",
      "Epoch 4994/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 154.0399\n",
      "Epoch 4995/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 153.9642\n",
      "Epoch 4996/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 153.8886\n",
      "Epoch 4997/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 153.8131\n",
      "Epoch 4998/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 153.7377\n",
      "Epoch 4999/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 153.6624\n",
      "Epoch 5000/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 153.5873\n",
      "[[29.924347]\n",
      " [30.987846]\n",
      " [32.1577  ]\n",
      " [33.221203]\n",
      " [34.284607]\n",
      " [35.454556]\n",
      " [35.35352 ]\n",
      " [37.58156 ]\n",
      " [38.645058]\n",
      " [39.814907]\n",
      " [40.87841 ]\n",
      " [42.2069  ]\n",
      " [43.117065]\n",
      " [44.175262]\n",
      " [45.238766]\n",
      " [46.40862 ]\n",
      " [47.472122]\n",
      " [48.535618]\n",
      " [49.705475]\n",
      " [50.768974]\n",
      " [51.832478]\n",
      " [53.002327]\n",
      " [54.06583 ]\n",
      " [55.129326]\n",
      " [56.192825]\n",
      " [57.362682]\n",
      " [58.426186]\n",
      " [59.516182]\n",
      " [60.659534]\n",
      " [61.723038]]\n",
      "[[56.423256]]\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.__version__)\n",
    "\n",
    "#  使用tf.keras 实现简单线性回归\n",
    "import pandas as pd\n",
    "data = pd.read_csv(\"../dataset/Income.csv\")\n",
    "print(data)\n",
    "x = data.Education\n",
    "y = data.Income\n",
    "model = tf.keras.Sequential()\n",
    "\n",
    "model.add(tf.keras.layers.Dense(1, input_shape=[1,]))\n",
    "print(model.summary())\n",
    "model.compile(optimizer='adam',\n",
    "              loss='mse')\n",
    "model.fit(x, y, epochs=5000)\n",
    "print(\n",
    "model.predict(x)\n",
    ")\n",
    "print(\n",
    "    model.predict(pd.Series([20]))\n",
    ")\n"
   ]
  }
 ],
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